Bottom Line:
Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript.Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments.By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Affiliation: School of Computing Sciences and School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

ABSTRACTSmall RNAs (sRNAs) are a class of short (20-25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

gks277-F3: Organization of partitioned 4-way tree entry points. Nodes at levels 10 and 11 within a 4-way tree data structure are collected and placed into labelled bins. There are a total of 16 bins as there are a total of 16 possible dinucleotide combinations. The label for each bin is the nucleotide at level 10 followed by the nucleotide at level 11. The bins hold entry points into the tree data structure. Entry nodes within a bin are used to partition the 4-way tree.

Mentions:
Once the sRNAs are encoded in the tree, target searches can be performed. The starting node for each search is the 10th node because we know that position 10 of the sRNA/target duplex must be complementary in order to cleave a target (6,29). Therefore pairs of nodes at levels 10 and 11 within the 4-way tree are collected and placed into labelled bins (Figure 3) according to the pair’s nucleotide composition. There are a total of 16 bins that correspond to the 16 possible dinucleotide combinations. Searches for sRNAs that could cause cleavage at a given degradome peak position are initiated by identifying the bin corresponding to nucleotides 10 and 11 of the candidate sequence. The tree is then traversed from nucleotide 10 towards the root. We place a restriction that once a walk up the tree from an entry point has occurred, the parent node of the entry point obtained from the bin may never be visited again during the current search and only descendent nodes of the entry point may be traversed. This restriction ensures that unnecessary nucleotide comparisons are not computed. We partition the tree by hiding all paths that have starting nodes in any of the other 15 labelled bins.Figure 3.

gks277-F3: Organization of partitioned 4-way tree entry points. Nodes at levels 10 and 11 within a 4-way tree data structure are collected and placed into labelled bins. There are a total of 16 bins as there are a total of 16 possible dinucleotide combinations. The label for each bin is the nucleotide at level 10 followed by the nucleotide at level 11. The bins hold entry points into the tree data structure. Entry nodes within a bin are used to partition the 4-way tree.

Mentions:
Once the sRNAs are encoded in the tree, target searches can be performed. The starting node for each search is the 10th node because we know that position 10 of the sRNA/target duplex must be complementary in order to cleave a target (6,29). Therefore pairs of nodes at levels 10 and 11 within the 4-way tree are collected and placed into labelled bins (Figure 3) according to the pair’s nucleotide composition. There are a total of 16 bins that correspond to the 16 possible dinucleotide combinations. Searches for sRNAs that could cause cleavage at a given degradome peak position are initiated by identifying the bin corresponding to nucleotides 10 and 11 of the candidate sequence. The tree is then traversed from nucleotide 10 towards the root. We place a restriction that once a walk up the tree from an entry point has occurred, the parent node of the entry point obtained from the bin may never be visited again during the current search and only descendent nodes of the entry point may be traversed. This restriction ensures that unnecessary nucleotide comparisons are not computed. We partition the tree by hiding all paths that have starting nodes in any of the other 15 labelled bins.Figure 3.

Bottom Line:
Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript.Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments.By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Affiliation:
School of Computing Sciences and School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

ABSTRACTSmall RNAs (sRNAs) are a class of short (20-25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.